Land surface versus ocean influence on atmospheric ... 1 ECMWF Seminar Proceedings Land surface

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    ECMWF Seminar Proceedings

    Land surface versus ocean influence

    on atmospheric variability and predictability

    at the seasonal timescale

    H. Douville, B. Decharme, S. Conil, Y. Peings

    Corresponding author address:

    Dr. Herv Douville

    CNRM/GMGEC/UDC, 42 avenue Coriolis

    31057 Toulouse Cedex 01, France

    Email: herve.douville@meteo.fr

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    1.Introduction

    Although the chaotic nature of the atmospheric dynamics (that is its sensitivity to

    initial conditions) is a fundamental limit to its deterministic predictability, it is now

    well known that predictability of seasonal weather statistics is possible (see reviews

    by Palmer and Anderson 1994). This arises from what may be termed external

    factors that alter the likelihood of residence in atmospheric attractors, enabling

    probabilistic forecasts to be made of the seasonal mean state, on condition that the

    external forcing is itself predictable.

    It is commonly accepted that the primary source of such external forcing at

    seasonal timescales arises from anomalous sea surface temperature (SST) patterns

    (i.e. Rowell 1998). On the one hand, these can indeed be predicted, either using

    coupled dynamical models or statistical models. On the other hand, both observational

    and numerical studies suggest that interannual atmospheric variability is partly driven

    by SST variability, especially in the tropical Pacific given the major teleconnections

    associated with the El Nio Southern Oscillation (i.e. Wallace et al. 1998).

    Further potential sources of predictive skill, in particular land surface anomalies,

    are generally believed to be much less important and are often neglected in studies

    about seasonal predictability. Nevertheless, an increasing body of literature suggests

    that land surface memory can also contribute to atmospheric variability and

    predictability at the monthly to seasonal timescale. The main objective of this paper is

    to relate a brief history of this research activity, to summarize some important results

    and to raise the issues to be further explored. It is not intended to be a comprehensive

    review of the subject, but rather to illustrate both advances and issues in the field,

    mainly using some of the studies conducted over the last few years at CNRM.

    2. Land surface models and data

    Land surface models (LSM) have evolved substantially from the original bucket

    model of Manabe (1969). While they were initially developed as simple

    parametrizations within the atmospheric general circulation models (GCM), their

    increasing complexity and the need of validating them in off-line mode (i.e. driven

    by more realistic atmospheric forcings than those simulated by climate models) has

    led to a progressive emancipation of LSMs that have moved from being subroutines

    to independent models which can be coupled to atmospheric models but also used

    off-line for other applications such as validation-calibration, but also hydrological

    predictions or impact studies (Polcher et al. 1998).

    The status of LSMs is therefore getting closer to that of oceanic GCMs, but does

    it mean that their influence on atmospheric variability is of comparable relevance?

    One crucial issue for answering this question is the lack of observational datasets to

    document the land surface variability at the global scale. While in situ and - since the

    1980s - satellite observations do exist to characterize the large-scale monthly SST

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    variability over the 20th

    century, the task is much more difficult over land for at least

    three reasons. First, the land influence on atmosphere depends on several parameters,

    not only surface temperature but also hydrology, vegetation and topography. Second,

    some of these parameters, especially but not only the subsurface hydrology, are still

    difficult to retrieve from satellite observations. Third, land surfaces generally show a

    much stronger high-frequency variability as well as a much stronger spatial

    heterogeneity than ocean surfaces, so that the monitoring of land surface variability

    with in situ observations only is simply not possible.

    Let us here examine the modeling implications of this last remark given the focus

    of the ECMWF seminar on the parametrization of subgrid physical processes. One of

    the main challenge in developing models for the land surface hydrology in numerical

    climate models stems indeed from the fact that the horizontal resolution of these

    models is incompatible with the characteristic scales of surface hydrologic properties.

    What cannot be explicitly resolved within the numerical model discretization must be

    parameterized. While in the 1980s, the development and local validation of LSMs has

    focused on improving the one-dimensional structure of the soil-snow-canopy system,

    more attention has been paid recently to the treatment of the horizontal subgrid

    variability of (i.e. Entekhabi and Eagleson 1989, Dmenil and Todini 1992, Koster

    and Suarez 1992).

    Fig. 1: The main sources of subgrid hydrological variability acoounted for in the ISBA-SGH land surface model of CNRM (Decharme and Douville 2006): precipitation, orography and vegetation.

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    Beyond numerical sensitivity studies showing the relevance of land surface

    heterogeneities for the calculation of the land surface energy and water fluxes, basin-

    scale field experiments have been a key stage in the development, calibration and

    validation of subgrid hydrological processes (i.e. Boone et al. 2004). River discharge

    observations indeed offer the opportunity to validate the simulated runoff (not all the

    components of the water budget) after integration over the drainage area. Depending

    on the size of the basin and on the frequency of interest, river routing models might be

    however necessary to account for the time lag between the production of runoff and

    the discharge response at the gauging station.

    At CNRM, a coherent subgrid hydrology, ISBA-SGH, accounting for grid cell

    heterogeneities not only in soil and vegetation properties (tile or mosaic approach,

    Koster et al. 1992) but also in precipitation intensities (Entekhabi and Eagleson 1989)

    and orography (Topmodel approach, Beven and Kerby 1979) has been developed and

    tested over the French Rhne river basin (Decharme and Douville 2006). Off-line

    simulations at 8km driven by the SAFRAN meteorological analysis of CNRM have

    been compared with lower resolution (1) simulations after linear interpolation of the

    high-resolution atmospheric forcing. For drainage areas exceeding a few 1 by 1 grid

    cells, results showed that the daily river discharge efficiencies of ISBA-SGH are not

    only less sensitive to horizontal resolution than those of the standard ISBA model, but

    also exceed at low resolution those obtained with ISBA at high resolution (Fig. 2).

    Fig. 2: Cumulative efficiency (Nash criterion) distribution of daily river discharges simulated at 88 gauge stations distributed over the Rhne river basin. All hydrological simulations are driven by observed atmospheric forcings either at a 8km (solid lines) or 1 (dashed lines) horizontal resolution and coupled to the MODCOU river routing model. ISBA-SGH (in red) is compared to the standard ISBA model (in black). From Decharme and Douville 2006.

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    Beyond basin-scale simulations, there is a need of validating and/or comparing

    LSMs at the global scale at which they are used in atmospheric GCMs. This was the

    purpose of the International Satellite Land Surface Climatology Project (ISLSCP) that

    has been launched in the mid-1990s and has allowed land surface modelers to produce

    global soil moisture climatologies by driving their models with common atmospheric

    forcings and land surface parameters in the framework of the Global Soil Wetness

    Project (GSWP, http://grads.iges.org/gswp).

    The ISBA land surface model of CNRM contributed to GSWP and was driven by

    the ISLSCP atmospheric forcings first from 1987 to 1988 (GSWP-1, Douville 1998),

    then from 1986 to 1995 (GSWP-2, Decharme and Douville 2007). Besides control

    runs using the common ISLSCP soil and vegetation parameters, parallel integrations

    have been achieved with the native ISBA land surface parameters to produce soil

    moisture and snow mass climatologies that are fully consistent with the CNRM

    atmospheric GCM. Such climatologies can be used to nudge global atmospheric

    simulations towards realistic land surface boundary conditions and compare the

    influence of soil moisture or snow mass versus SST on atmospheric variability and

    predictability at the seasonal timescale (see next section).

    Fig. 3: Cumulative efficiency (Nash criterion) distribution of monthly river discharge simulated from 1986 to 1995 at 80 gauge stations distributed over the largest (>100000 km) worlds river basins. Six land surface models are compared : ISBA-SGH (NEW) and ISBA-standard (dt92), as well as four other LSMs having contributed to the international GSWP-2 intercomparison project (B3 atmospheric forcing). Runoff from all simulations has been converted into river discharge using the TRIP river routing model (Oki and Sud 1998). From Decharme and Douville 2007.

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    Moreover, the off-line GSWP simulations were also useful to test the ISBA-SGH

    hydrology at th